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1. Now You Feel It, Now You Don’t: The Effect of Movement, Cue Complexity, and Body Location on Tactile Change Detection

   https://doi.org/10.1177/0018720819850278  

   Gomes, Kylie ; Betza, Scott ; Riggs, Sara_Lu ( June 2019 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   To evaluate the effects that movement, cue complexity, and the location of tactile displays on the body have on tactile change detection.

   Tactile displays have been demonstrated as a means to address data overload by offloading the visual and auditory modalities. However, change blindness—the failure to detect changes in a stimulus when changes coincide with another event or disruption in stimulus continuity—has been demonstrated to affect the tactile modality and may be exacerbated during movement. The complexity of tactile cues and locations of tactile displays on the body may also affect the detection of changes in tactile patterns. Limitations to tactile perception need to be examined.

   Twenty-four participants performed a tactile change detection task while sitting, standing, and walking. Tactile cues varied in complexity and included low, medium, and high complexity cues presented to the arm or back.

   Movement adversely affects tactile change detection as hit rates were the highest while sitting, followed by standing and walking. Cue complexity affected tactile change detection: Low complexity cues resulted in higher detection rates compared with medium and high complexity cues. The arms exhibited better change detection performance than the back.

   The design of tactile displays should consider the effect of movement. Cue complexity should be minimized and decisions about the location of a tactile display should take into account body movements to support tactile perception.

   The findings can provide design guidelines to inform tactile display design for data-rich, complex domains.

    

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2. Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces from Images

   https://doi.org/10.1007/978-3-030-58583-9_1  

   Purri, Matthew and ( November 2020 , European Conference on Computer Vision)

   null (Ed.)

   The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we introduce a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property. 

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3. Identifying Benign and Malignant Breast Tumor Using Vibro-acoustic Tactile Imaging Sensor

   https://doi.org/10.1109/SENSORS52175.2022.9967083  

   Rahman, Nazia ; Won, Chang-hee ( October 2022 , IEEE Sensors Conference)

   Tactile imaging sensor determines the tumor's mechanical properties such as size, depth, and Young's modulus based on the principle of total internal reflection of light. To improve the classifying accuracy of the Tactile imaging sensor, we introduce ultrasound signals and estimate the difference in the tumor tactile images. A developed vibro-acoustic tactile imaging sensor was used to classify benign and malignant tumors. We test the developed system on breast tumor phantoms. These vibrated tactile images are analyzed to improve the overall performance of tumor detection. 

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4. RoboGraphics: Dynamic Tactile Graphics Powered by Mobile Robots

   https://doi.org/10.1145/3308561.3353804  

   Guinness, Darren ; Muehlbradt, Annika ; Szafir, Daniel ; Kane, Shaun K. ( October 2019 , ASSETS '19: The 21st International ACM SIGACCESS Conference on Computers and Accessibility)

   Tactile graphics are a common way to present information to people with vision impairments. Tactile graphics can be used to explore a broad range of static visual content but aren’t well suited to representing animation or interactivity. We introduce a new approach to creating dynamic tactile graphics that combines a touch screen tablet, static tactile overlays, and small mobile robots. We introduce a prototype system called RoboGraphics and several proof-of-concept applications. We evaluated our prototype with seven participants with varying levels of vision, comparing the RoboGraphics approach to a flat screen, audio-tactile interface. Our results show that dynamic tactile graphics can help visually impaired participants explore data quickly and accurately. 

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5. The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms

   https://doi.org/10.1109/IROS47612.2022.9981915  

   Koenig, Alexander ; Liu, Zixi ; Janson, Lucas ; Howe, Robert ( October 2022 , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   A long-standing question in robot hand design is how accurate tactile sensing must be. This paper uses simulated tactile signals and the reinforcement learning (RL) framework to study the sensing needs in grasping systems. Our first experiment investigates the need for rich tactile sensing in the rewards of RL-based grasp refinement algorithms for multi-fingered robotic hands. We systematically integrate different levels of tactile data into the rewards using analytic grasp stability metrics. We find that combining information on contact positions, normals, and forces in the reward yields the highest average success rates of 95.4% for cuboids, 93.1% for cylinders, and 62.3% for spheres across wrist position errors between 0 and 7 centimeters and rotational errors between 0 and 14 degrees. This contact-based reward outperforms a non-tactile binary-reward baseline by 42.9%. Our follow-up experiment shows that when training with tactile-enabled rewards, the use of tactile information in the control policy’s state vector is drastically reducible at only a slight performance decrease of at most 6.6% for no tactile sensing in the state. Since policies do not require access to the reward signal at test time, our work implies that models trained on tactile-enabled hands are deployable to robotic hands with a smaller sensor suite, potentially reducing cost dramatically. 

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